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We utilized the BAAI\/bge-m3 model to generate high-dimensional embeddings, employing cosine similarity to measure contrastive linguistic distances. To evaluate classification performance, we compared various architectures, including XGBoost, LSTM, and Logistic Regression (LR), across three categories: Exact, Semi-Exact, and Different. While deep learning and ensemble methods were initially considered, experimental results on an expanded dataset demonstrated that a fine-tuned LR model provided superior stability and generalization. The optimized LR approach significantly increased classification accuracy from 89% to 96% (p\u2009&lt;\u20090.05). These findings suggest that high-quality embeddings can enhance detection performance in low-resource contexts without the computational overhead of complex architectures. To ensure transparency and reproducibility, our dataset and source code are available on GitHub.<\/jats:p>","DOI":"10.1371\/journal.pone.0354459","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T17:26:12Z","timestamp":1790789172000},"page":"e0354459","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["English-Persian cross-language plagiarism detection: Multilingual deep learning approach"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6226-7497","authenticated-orcid":true,"given":"Sara","family":"Ansar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9284-7329","authenticated-orcid":true,"given":"Marzieh","family":"Zarinbal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6443-7450","authenticated-orcid":true,"given":"Azadeh","family":"Mohebi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"issue":"4","key":"pone.0354459.ref001","first-page":"1823","article-title":"Detecting similarity in paraphrased Persian texts using semantic and probabilistic methods","volume":"34","author":"N Pakniat","year":"2019","journal-title":"Iranian Journal of Information Processing and Management"},{"key":"pone.0354459.ref002","unstructured":"Kasprzak J, Brandejs M. Improving the reliability of the plagiarism detection system. 2010."},{"key":"pone.0354459.ref003","unstructured":"Pataki M. A new approach for searching translated plagiarism. 2012."},{"key":"pone.0354459.ref004","article-title":"Searching for Translated Plagiarism with the Help of Desktop Grids","author":"M Pataki","year":"2012","journal-title":"J Grid Computing"},{"key":"pone.0354459.ref005","doi-asserted-by":"crossref","unstructured":"Kent CK, Salim N. Web based cross language semantic plagiarism detection. 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